Complete Algorithms Complexity and Big O Notation Course from Udemy

What's inside

Learning objectives

Algorithm time and space complexity evaluation
Big o notation
Interview coding tasks review
Time complexity cases
Time complexities comparison
Mathematical comparison of functions

Typical complexities evaluation
Log n complexity
Strings and complexity evaluation
Recursive algorithms complexity
Amortized analysis
Space complexity

Algorithm time and space complexity evaluation
Big o notation
Interview coding tasks review
Time complexity cases
Time complexities comparison
Mathematical comparison of functions
Typical complexities evaluation
Log n complexity
Strings and complexity evaluation
Recursive algorithms complexity
Amortized analysis
Space complexity

Syllabus

Complexity Evaluation

Contents

Algorithm complexity importance
Reasons to study Big O
Mathematical function in basic terms
Real-life example of mathematical function usage

Algorithm complexity (computational complexity)
Algorithm complexity types
Time complexity of algorithms
Space complexity of algorithms
Algorithm running time
Time complexity function

Best case complexity
Worst case complexity

Comparison of the complexities of two algorithms
Comparison problems

Order of a function
Comparison of functions orders
Finding a function with a lower order

Big O notation (Big O, Big Oh)
Big O explained
Classifying functions using Big O notation
Goals of algorithm complexity evaluation
Constants and algorithm complexity
Big O notation issues
Big O arithmetic operations
Algorithm complexity classes
Constant time complexity, O(1)
Logarithmic time complexity, O(log N) (log N complexity)
Sublinear time complexity, O(sqrt(N))
Linear time complexity, O(N)
Linearithmic time complexity, O(N * log N)
Quadratic time complexity, O(N^2)
Exponential time complexity, O(2^N)
Factorial time complexity, O(N!)

Arithmetic progression
Geometric progression
Logarithm
Factorial

Algorithm complexity of sequential operations
Algorithm complexity of nested operations

Logarithmic algorithm complexity, O(log N)
Binary search algorithm complexity

Internal strings algorithms and their impact on resulting complexity

Recursive function time complexity
Recursive algorithms time complexity

Amortized analysis and its usage
Aggregate method of amortized analysis

Space complexity
Recursive function space complexity
Recursive algorithms space complexity

Detailed examples of algorithm complexity analysis that allow to consolidate the concept of algorithm complexity and approaches to its evaluation

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores Big O notation, which is fundamental for evaluating algorithm performance and is a core concept in computer science curricula

Covers time and space complexity, which are essential for optimizing code and managing resources effectively in software development

Includes examples found in interviews at Google, Facebook, and Amazon, making it relevant for job preparation in the tech industry

Examines amortized analysis, which is a technique used to analyze the average performance of algorithms over a sequence of operations

Requires basic programming skills, suggesting that learners should have some prior experience with coding before taking this course

Focuses on mathematical comparisons of functions, which may require learners to have a solid foundation in mathematics and discrete math

Reviews summary

Fundamentals of big o and complexity

Based on the course description, students might find this course provides clear explanations of Big O notation and algorithm complexity, focusing on the mathematical basis and various complexity classes. The inclusion of interview coding tasks suggests practical relevance for career development. Learners could appreciate its aim to be independent, requiring only basic programming skills. Key topics like recursion and amortized analysis are covered. However, students seeking very advanced topics or extensive hands-on coding practice beyond the provided examples might need supplementary resources, as the course emphasizes simple language suitable for beginners.

Provides examples from interviews.

"The inclusion of examples found in Google, Facebook, Amazon interviews is a strong point."

"Looking forward to applying the concepts to interview-style problems."

"Practical examples are key to understanding complexity evaluation methods."

Aims for simple, clear explanations.

"I appreciate the promise of 'simple language' to explain the math behind complexity."

"It seems designed to break down complex ideas into an effective form for understanding."

"The course material appears reworked from books and articles for better perception."

Includes essential complexity topics.

"The syllabus covers all the major Big O notation classes and complexity types."

"Topics like recursion and amortized analysis are included, which is good."

"It seems to provide a solid foundation in evaluating time and space complexity."

May be too basic for advanced learners.

"Given the focus on 'simple language' and 'basic programming skills', I wonder if it covers advanced optimization techniques sufficiently."

"It seems designed for those new to the topic, potentially lacking depth for experienced developers."

"I hope the 'independent' nature means it's thorough, but it might require external practice for mastery."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Algorithms Complexity and Big O Notation Course with these activities:

Review Data Structures

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Reinforce your understanding of fundamental data structures to better grasp algorithm complexity analysis.

Browse courses on Data Structures

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Review the definitions and properties of common data structures.
Implement basic operations (insertion, deletion, search) for each data structure.
Analyze the time and space complexity of these operations.

LeetCode Easy Problems

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Practice applying Big O notation to analyze the efficiency of solutions to coding problems.

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Solve easy-level problems on LeetCode or similar platforms.
Analyze the time and space complexity of your solutions.
Compare your solutions with others and identify areas for improvement.

Cracking the Coding Interview

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Practice algorithm complexity analysis with interview-style coding problems.

View Cracking the Coding Interview: 189 Programming... on Amazon

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Select problems from the book that focus on algorithm complexity.
Solve the problems and analyze the time and space complexity of your solutions.
Compare your solutions with the book's solutions and explanations.

Four other activities

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Help others in the course discussion forum

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Reinforce your understanding of Big O notation by explaining concepts to other students.

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Regularly check the course discussion forum for questions related to Big O notation.
Provide clear and concise explanations to help other students understand the concepts.
Offer examples and code snippets to illustrate the concepts.

Introduction to Algorithms

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Deepen your understanding of algorithm complexity with a comprehensive textbook.

View Introduction to Algorithms, fourth edition on Amazon

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Read relevant chapters on algorithm analysis and Big O notation.
Work through the examples and exercises provided in the book.
Compare the book's explanations with the course materials.

Big O Notation Cheat Sheet

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Create a visual guide summarizing common Big O complexities and their characteristics.

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Research and compile a list of common Big O complexities (O(1), O(log n), O(n), etc.).
For each complexity, provide a brief explanation, example code snippet, and a graph illustrating its growth rate.
Design the cheat sheet for easy readability and quick reference.

Implement Sorting Algorithms and Compare Performance

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Solidify your understanding of Big O by implementing and comparing the performance of different sorting algorithms.

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Implement several sorting algorithms (e.g., bubble sort, insertion sort, merge sort, quicksort).
Measure the execution time of each algorithm for different input sizes.
Plot the results and compare the observed performance with the theoretical Big O complexity.

Career center

Learners who complete Complete Algorithms Complexity and Big O Notation Course will develop knowledge and skills that may be useful to these careers:

Algorithm Developer

Algorithm developers create and refine algorithms for various applications. This course on algorithms complexity and Big O notation helps algorithm developers design optimal solutions. A deep understanding of algorithm complexity is critical for creating efficient algorithms that meet performance requirements. The course's treatment of Big O notation, time complexity cases, and mathematical comparison of functions provides a solid foundation for algorithm design. Learning about typical complexities evaluation and logarithmic complexity is especially valuable for algorithm developers. The interview coding tasks review segment of this course may give algorithm developers a boost when applying for a new job.

See salaries and explore the career path for Algorithm Developer

Performance Engineer

Performance engineers analyze and improve the performance of software systems. This course on algorithms complexity and Big O notation could be useful for performance engineers to identify and resolve performance bottlenecks. A deep understanding of algorithm complexity is essential for diagnosing performance issues and optimizing code. The course's treatment of Big O notation, time complexity cases, and space complexity provides a comprehensive toolkit for performance analysis. The course's examples of algorithm complexity analysis helps performance engineers to consolidate the concept of evaluating algorithms.

See salaries and explore the career path for Performance Engineer

Software Engineer

A software engineer designs, develops, and tests software applications. This course on algorithms complexity and Big O notation helps software engineers write efficient and performant code. Understanding time and space complexity, as covered in the course, is crucial for optimizing algorithms and ensuring that software scales effectively. The course's focus on algorithm complexity evaluation and the analysis of various complexity classes, like logarithmic or quadratic, helps engineers to choose the right algorithms for specific tasks. This course may provide a competitive edge in coding interviews, as it reviews example interview tasks seen at technology companies.

See salaries and explore the career path for Software Engineer

Software Architect

Software Architects design the structure of software systems to meet business requirements. This algorithms complexity and Big O notation course helps software architects design scalable and efficient software systems. Understanding algorithm complexity is crucial for making informed decisions about the design of software components and their interactions. The course's coverage of time complexity cases and space complexity assists architects in building software that performs well under varying loads. The course's content is also helpful for identifying and mitigating potential performance bottlenecks during the design phase.

See salaries and explore the career path for Software Architect

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. The algorithms complexity and Big O notation course aids machine learning engineers to build scalable and efficient models. Understanding algorithm complexity is essential for optimizing training and inference times, especially when working with large datasets and complex models. The course's exploration of recursive algorithms complexity and time complexity cases allows machine learning engineers to fine-tune their models for optimal performance. The discussion of space complexity helps in managing memory usage, particularly in resource-constrained environments.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data scientists analyze large datasets to extract meaningful insights. The algorithms complexity and Big O notation course can assist data scientists in optimizing their data processing workflows. Data scientists often work with algorithms that have varying time and space complexities. The course's coverage of topics such as amortized analysis and space complexity helps data scientists select and implement algorithms to efficiently handle large datasets. This course may be useful for optimizing data analysis pipelines and improving the performance of machine learning models, particularly through the understanding of typical complexities evaluation.

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Game Developer

A game developer creates the software that makes video games run. This course on algorithms complexity and Big O notation enables game developers to optimize game performance. Understanding algorithm complexity is critical for creating games that run smoothly and efficiently, especially when dealing with complex simulations and large amounts of data. The course helps game developers choose the right algorithms for tasks such as collision detection, pathfinding, and rendering. The course's coverage of recursive algorithms complexity and space complexity is particularly relevant for optimizing game performance. Additionally, this course may provide a competitive edge in coding interviews for game development roles.

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Data Engineer

Data engineers build and maintain the infrastructure for data storage and processing. This algorithms complexity and Big O notation course is applicable as it helps data engineers optimize data pipelines and storage solutions. Understanding algorithm complexity is important for designing efficient and scalable data processing systems. The course's coverage of topics such as time complexity cases and amortized analysis can help data engineers make informed decisions about data storage and retrieval strategies. For example, data engineers can use knowledge of logarithmic time complexity to optimize database indexing strategies.

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Robotics Engineer

Robotics engineers design, build, and program robots for various applications. The algorithms complexity and Big O notation course may assist robotics engineers to optimize robot control algorithms and path planning. Understanding algorithm complexity is critical for creating efficient and responsive robot control systems. Topics covered in the course, such as recursive algorithms complexity and space complexity, can help robotics engineers optimize their code for real-time performance. The knowledge gained here can also be applied to improve the efficiency of path planning algorithms, especially in environments with limited computational resources.

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Firmware Engineer

Firmware engineers develop low-level software that controls hardware devices. A course on algorithms complexity and Big O notation may help firmware engineers optimize their code for resource-constrained environments. Understanding algorithm complexity is crucial for writing efficient firmware that can run on embedded systems with limited memory and processing power. The course's treatment of topics such as space complexity and recursive algorithms complexity helps firmware engineers create compact and efficient code. The course's review of recursive algorithms complexity is useful in firmware development where recursion is often avoided due to resource constraints.

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Systems Architect

Systems architects design and implement complex computer systems. The algorithms complexity and Big O notation course provides systems architects with the knowledge to design scalable and efficient systems. Understanding algorithm complexity is important for optimizing the performance of various system components. The course's treatment of topics such as time complexity cases and space complexity helps architects make informed decisions about system design and resource allocation. The course's coverage of various complexities from constant to factorial helps architects understand tradeoffs when designing systems.

See salaries and explore the career path for Systems Architect

Quantitative Analyst

Quantitative analysts, often working in finance, develop and implement mathematical models for trading and risk management. This course on algorithms complexity and Big O notation helps quantitative analysts optimize their models and trading strategies. Understanding algorithm complexity is critical for creating efficient algorithms that can process large amounts of data in real-time. The course's discussion of logarithmic time complexity and amortized analysis may assist quantitative analysts in designing high-performance trading systems. Prior knowledge of mathematical function is helpful.

See salaries and explore the career path for Quantitative Analyst

Database Administrator

Database administrators manage and maintain database systems, ensuring data integrity and performance. A course on algorithms complexity and Big O notation may help database administrators optimize database queries and operations. Understanding the time and space complexity of database operations is essential for ensuring that databases can handle large amounts of data efficiently. The course's discussion of topics such as logarithmic time complexity and amortized analysis helps database administrators make informed decisions about database design and query optimization. Additionally, the section on space complexity helps manage storage effectively.

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DevOps Engineer

DevOps engineers automate and streamline software development and deployment processes. This algorithms complexity and Big O notation course is useful for DevOps engineers to optimize their infrastructure and deployment pipelines. Understanding algorithm complexity can improve the performance of automation scripts and deployment tools. The course's coverage of topics such as time complexity cases and space complexity may help DevOps engineers design efficient and scalable infrastructure solutions. The course's discussion of strings and complexity evaluation helps with text parsing and processing in configuration management.

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Security Engineer

Security Engineers protect computer systems and networks from threats. This algorithms complexity and Big O notation course may be helpful for security engineers looking to secure their systems. An understanding of algorithm complexity is essential for building effective security tools and algorithms. The course's exploration of topics such as time complexity cases and strings analysis helps analyze and optimize security algorithms. This course may provide a great introduction to the concepts of cryptography and cybersecurity.

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Complete Algorithms Complexity and Big O Notation Course

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Fundamentals of big o and complexity

Activities

Career center

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